System and method for analytically modeling data organized according to a referenced attribute

ABSTRACT

A system and method for analytically modeling data in a data cube is disclosed. A first dimension of the data cube is modeled according to a first table having a referencing attribute. A second dimension of the data cube is modeled according to a second table having a referenced attribute. The first dimension is tied to the second dimension according to the referencing attribute and the referenced attribute.

BACKGROUND OF THE INVENTION

[0001] 1. Field of the Invention

[0002] The present invention relates to systems and methods foranalytically modeling data organized and stored in a relationaldatabase, and, more particularly, to analytically modeling dataorganized according to a referenced attribute.

[0003] 2. Description of the Prior Art

[0004] Online analytical processing (OLAP) is a key part of many datawarehouse and business analysis systems. OLAP services provide for fastanalysis of multidimensional information. For this purpose, OLAPservices provide for multidimensional access and navigation of data inan intuitive and natural way, providing a global view of data that canbe drilled down into particular data of interest. Speed and responsetime are important attributes of OLAP services that allow users tobrowse and analyze data online in an efficient manner. Further, OLAPservices typically provide analytical tools to rank, aggregate, andcalculate lead and lag indicators for the data under analysis.

[0005] In this context, an OLAP cube may be modeled according to auser's perception of the data. The cube may have multiple dimensions,each dimension modeled according to attributes of the data. Typically,there is a hierarchy associated with each dimension. For example, a timedimension can consist of years subdivided into months subdivided intoweeks subdivided into days, while a geography dimension can consist ofcountries subdivided into states subdivided into cities. Dimensionmembers act as indices for identifying a particular cell or range ofcells within the cube.

[0006] OLAP services are often used to analytically model data that isstored in a relational database such as, for example, an OnlineTransactional Processing (OLTP) database. Data stored in a relationaldatabase may be organized according to multiple tables with each tablehaving data corresponding to a particular data type. A tablecorresponding to a particular data type may be organized according tocolumns corresponding to data attributes. For example, datacorresponding to the type “Customer” may be organized in a “Customer”table with columns “Customer ID” and “Location ID”. Data correspondingto the type “Location” may be organized in a “Location” table withcolumns “Location ID”, “City”, and “State”.

[0007] The “Customer” table may be referred to as a “referencing” table,and the “Location” table may be referred to as a “referenced” tablebecause data in the “Location” table is referenced by corresponding datain the “Customer” table. To obtain location data for a particularcustomer, the “Location ID” for the customer may be obtained based onthe customer's “Customer ID” in the “Customer” table. Next, an entryhaving the obtained “Location ID” is found in the “Location” table basedon the “Location ID” attribute thereof. Finally, from such found entry,the “State” and “City” are obtained for the “Customer ID” and customer.

[0008] One issue that arises with regard to analytically modeling datafrom a relational database is how to best model data with such areferenced table. In existing methods for analytically modeling datawith a referenced table, a single dimension is modeled according to botha particular referencing table and a referenced table. The singledimension has a dimension hierarchy that includes attributes in thereferencing table, which may be referred to as “referencing attributes”and attributes in the referenced table, which may be referred to as“referenced attributes”. For example, in such an existing method, anOLAP cube has a “Customer” dimension modeled according to both the“Customer” table and the “Location” table. The “Customer” dimension hasa dimension hierarchy including “State” subdivided by “City” subdividedby “Customer ID”.

[0009] Including the referenced attributes in the dimension hierarchy ofthe single dimension is repetitive because multiple data entriescorresponding to referenced attributes may have identical values. Forexample, with regard to the “Customer” and “Location” tables, multiplecustomers may reside in the same state and/or city. This repetition isexacerbated when multiple referencing tables refer to a singlereferenced table. This repetition would be eliminated if, rather thanincluding referenced attributes in the dimension hierarchy of eachdimension, referenced attributes were included only in the dimensionhierarchy of a “referenced dimension” modeled according to thereferenced table and referred to by the referencing dimension. Suchelimination of repetition results in a reduction of data storage sizeand a reduction of the time required for modeling the cube.

SUMMARY OF THE INVENTION

[0010] Accordingly, in the present invention, a system and method foranalytically modeling data with a referenced attribute is disclosed. Ina relational database, a first table organizes a first type according toa first attribute. A second table organizes a second type according tothe first attribute and a second attribute. A third table organizes athird type according a third attribute. The second attribute of thesecond table is related to the third attribute of the third table suchthat the second table may be cross-referenced to the third tablethereby.

[0011] The data stored in the relational database is analyticallymodeled. A measure is modeled according to the first type of the firsttable. A first dimension is modeled according to the second type of thesecond table. A second dimension is modeled according to the third typeof the third table. The measure is tied to the first dimension accordingto the first attribute of the first and second tables to allow themeasure to be analyzed by the first dimension according to the firstattribute. The first dimension is tied to the second dimension accordingto the second attribute of the second table and the third attribute ofthe third table to allow the measure to be analyzed by the seconddimension according to the second and third attribute. Thus, the firstdimension references the second dimension.

BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The illustrative embodiments will be better understood afterreading the following detailed description with reference to theappended drawings, in which:

[0013]FIG. 1 is a block diagram representing a general purpose computersystem in which aspects of the present invention and/or portions thereofmay be incorporated;

[0014]FIG. 2 is a sample relational database table corresponding to“Purchase” data;

[0015]FIG. 3 is a sample relational database table corresponding to“Customer” data;

[0016]FIG. 4 is a sample relational database table corresponding to“Location” data;

[0017]FIG. 5 is a prior art analytical data cube derived from the tablesof FIGS. 2-4;

[0018]FIG. 6 is a hierarchical data tree showing data organized in adimension of the cube of FIG. 5 according to a plurality of gradations;

[0019]FIG. 7 is an analytical data cube derived from the tables of FIGS.2-4 in accordance with one embodiment of the present invention; and

[0020]FIG. 8 is a hierarchical data tree showing data organized in adimension of the cube of FIG. 7 according to a plurality of gradations.

[0021]FIG. 9 is a hierarchical data tree showing data organized in adimension of the cube of FIG. 7 according to a plurality of gradations.

DETAILED DESCRIPTION

[0022] A system and method for analytically modeling data with relatedattributes is disclosed below with reference to the aforementioneddrawings. Those skilled in the art will readily appreciate that thedescription given herein with respect to those drawings is forexplanatory purposes only and is not intended in any way to limit thescope of the invention to the specific embodiments shown. Throughout thedescription, like reference numerals are employed to refer to likeelements in the respective figures.

[0023] Computer Environment

[0024]FIG. 1 and the following discussion are intended to provide abrief general description of a suitable computing environment in whichthe present invention and/or portions thereof may be implemented.Although not required, the invention is described in the general contextof computer-executable instructions, such as program modules, beingexecuted by a computer, such as a client workstation or a server.Generally, program modules include routines, programs, objects,components, data structures and the like that perform particular tasksor implement particular abstract data types. Moreover, it should beappreciated that the invention and/or portions thereof may be practicedwith other computer system configurations, including hand-held devices,multi-processor systems, microprocessor-based or programmable consumerelectronics, network PCs, minicomputers, mainframe computers and thelike. The invention may also be practiced in distributed computingenvironments where tasks are performed by remote processing devices thatare linked through a communications network. In a distributed computingenvironment, program modules may be located in both local and remotememory storage devices.

[0025] As shown in FIG. 1, an exemplary general purpose computing systemincludes a conventional personal computer 120 or the like, including aprocessing unit 121, a system memory 122, and a system bus 123 thatcouples various system components including the system memory to theprocessing unit 121. The system bus 123 may be any of several types ofbus structures including a memory bus or memory controller, a peripheralbus, and a local bus using any of a variety of bus architectures. Thesystem memory includes read-only memory (ROM) 124 and random accessmemory (RAM) 125. A basic input/output system 126 (BIOS), containing thebasic routines that help to transfer information between elements withinthe personal computer 120, such as during start-up, is stored in ROM124.

[0026] The personal computer 120 may further include a hard disk drive127 for reading from and writing to a hard disk (not shown), a magneticdisk drive 128 for reading from or writing to a removable magnetic disk129, and an optical disk drive 130 for reading from or writing to aremovable optical disk 131 such as a CD-ROM or other optical media. Thehard disk drive 127, magnetic disk drive 128, and optical disk drive 130are connected to the system bus 123 by a hard disk drive interface 132,a magnetic disk drive interface 133, and an optical drive interface 134,respectively. The drives and their associated computer-readable mediaprovide non-volatile storage of computer readable instructions, datastructures, program modules and other data for the personal computer120.

[0027] Although the exemplary environment described herein employs ahard disk, a removable magnetic disk 129, and a removable optical disk131, it should be appreciated that other types of computer readablemedia which can store data that is accessible by a computer may also beused in the exemplary operating environment. Such other types of mediainclude a magnetic cassette, a flash memory card, a digital video disk,a Bernoulli cartridge, a random access memory (RAM), a read-only memory(ROM), and the like.

[0028] A number of program modules may be stored on the hard disk,magnetic disk 129, optical disk 131, ROM 124 or RAM 125, including anoperating system 135, one or more application programs 136, otherprogram modules 137 and program data 138. A user may enter commands andinformation into the personal computer 120 through input devices such asa keyboard 140 and pointing device 142. Other input devices (not shown)may include a microphone, joystick, game pad, satellite disk, scanner,or the like. These and other input devices are often connected to theprocessing unit 121 through a serial port interface 146 that is coupledto the system bus, but may be connected by other interfaces, such as aparallel port, game port, or universal serial bus (USB). A monitor 147or other type of display device is also connected to the system bus 123via an interface, such as a video adapter 148. In addition to themonitor 147, a personal computer typically includes other peripheraloutput devices (not shown), such as speakers and printers. The exemplarysystem of FIG. 1 also includes a host adapter 155, a Small ComputerSystem Interface (SCSI) bus 156, and an external storage device 162connected to the SCSI bus 156.

[0029] The personal computer 120 may operate in a networked environmentusing logical connections to one or more remote computers, such as aremote computer 149. The remote computer 149 may be another personalcomputer, a server, a router, a network PC, a peer device or othercommon network node, and typically includes many or all of the elementsdescribed above relative to the personal computer 120, although only amemory storage device 150 has been illustrated in FIG. 1. The logicalconnections depicted in FIG. 1 include a local area network (LAN) 151and a wide area network (WAN) 152. Such networking environments arecommonplace in offices, enterprise-wide computer networks, intranets,and the Internet.

[0030] When used in a LAN networking environment, the personal computer120 is connected to the LAN 151 through a network interface or adapter153. When used in a WAN networking environment, the personal computer120 typically includes a modem 154 or other means for establishingcommunications over the wide area network 152, such as the Internet. Themodem 154, which may be internal or external, is connected to the systembus 123 via the serial port interface 146. In a networked environment,program modules depicted relative to the personal computer 120, orportions thereof, may be stored in the remote memory storage device. Itwill be appreciated that the network connections shown are exemplary andother means of establishing a communications link between the computersmay be used.

[0031] System and Method of the Present Invention

[0032] An analytical data service such as, for example, On-LineAnalytical Processing (OLAP) may be employed to model data stored in arelational database such as, for example, an On-Line TransactionalDatabase (OLTP). As set forth previously, data stored in a relationaldatabase may be organized according to multiple tables, with each tablehaving data corresponding to a particular data type. A tablecorresponding to a particular data type may be organized according tocolumns corresponding to data attributes. One such table is shown inFIG. 2, with data corresponding to the type “Purchase” organized in a“Purchase” table 200 with columns including “Customer ID” 210 and“Purchase Quantity” 212 among others. Another such table is shown inFIG. 3, with data corresponding to the type “Customer” organized in a“Customer” table 300 with columns “Customer ID” 310 and “Location ID”312. Another such table is shown in FIG. 4, with data corresponding tothe type “Location” organized in a “Location” table 400 with columns“Location ID” 410, “City” 412, and “State” 414.

[0033] “Customer” table 300 may be referred to as a “referencing” table,and “Location” table 400 may be referred to as a “referenced” tablebecause data in “Location” table 400 is referenced by corresponding datain “Customer” table 300. To obtain location data for a particularcustomer, the “Location ID” 312 for the customer may be obtained basedon the customer's “Customer ID” 310 in “Customer” table 300. Next, anentry having the obtained “Location ID” 312 is found in “Location” table400 based on the “Location ID” attribute 410 thereof. Finally, from suchfound entry, “State” 414 and “City” 412 are obtained for the “CustomerID” 310 and customer. As should be appreciated, other referencing tables(not shown) that reference “Location” table 400 may also be present.

[0034] “Purchase” table 200 organizes each purchase according to a“Customer ID” 210 such as “Customer ID” 310 in “Customer” table 300.Accordingly, “Purchase” table 200 may also have additional columns (notshown) corresponding to additional attributes relevant to each purchase.

[0035] Referring now to FIG. 5, a prior art model of the data from“Purchase” table 200, “Customer” table 300, and “Sales” table 400 isshown as organized into a data cube 500. Cube 500 has a measure“Purchase” 505 modeled according to “Purchase” table 200. Cube 500 alsohas a dimension 510 modeled according to referencing “Customer” table300 and referenced “Location” table 400. The measure “Purchase” 505 istied to the dimension “Customer” 510 according to the attribute“Customer ID” 210, 310 of tables 200, 300. This relationship is shown inFIG. 5 by the line connecting “Purchase” measure 505 and “Customer”dimension 510. As should be appreciated, other dimensions (not shown)may also be present.

[0036] As shown, “Customer” dimension 510 has a dimension hierarchy thatincludes both “referencing” attributes from referencing “Customer” table300 and “referenced” attributes from referenced “Location” table 400. Asshould be appreciated, Location ID attribute 312 of FIG. 3 and 410 ofFIG. 4 is both a referencing attribute and a referenced attribute, and,therefore, acts as a cross-referencing attribute that need notnecessarily be included in the hierarchy. The dimension hierarchy ofreferencing “Customer” dimension 510 is represented by data tree 600 asshown in FIG. 6. Nodes 610 and 611 in the top row are “State” nodescorresponding to “State” attribute 414 in FIG. 4. Nodes 620-624 in thesecond row are “City” nodes corresponding to “City” attribute 412 inFIG. 4. Nodes 630-639 in the third row are “Customer ID” nodescorresponding to “Customer ID” attribute 310 in FIG. 3. As should beappreciated, one advantage of a hierarchically organized analytical datamodel is that such a model allows data to be aggregated based on aquery. In particular, data aggregated according to the dimensionhierarchy of FIG. 7 may be aggregated based on any of the levels in thehierarchy. For example, if a query requires a purchase quantity for aspecific city, then data may be aggregated by “City” attribute 412 andthe second row of data tree 600.

[0037] Including the referenced attributes in the dimension hierarchy ofdimension 510 of cube 500 is repetitive because multiple data entriescorresponding to a referenced attribute may have identical values. Forexample, with regard to “Customer” table 300 and “Location” table 400,multiple customers may reside in the same state and/or city. Thisrepetition is exacerbated when multiple referencing tables refer to asingle referenced table 400. This repetition would be eliminated if,rather than including referenced attributes in the dimension hierarchyof each dimension, referenced attributes were included only in thedimension hierarchy of a single “referenced dimension” modeled accordingto referenced table 400 and referred to by each referencing dimension.

[0038] Referring now to FIG. 7, analytical data cube 700 is ananalytical model of the data from “Customer” table 300 and “Location”table 400 in accordance with one embodiment of the present invention.Cube 700 has a measure “Purchase” 705 modeled according to “Purchase”table 200. Cube 700 also has both a referencing “Customer” dimension 710modeled according to “Customer” table 300 of FIG. 3 and a referenced“Location” dimension 712 modeled according to “Location” table 400 ofFIG. 4. As should be appreciated, other dimensions (not shown) may bemodeled according to other tables (not shown). Unlike prior art datacube 500 of FIG. 5 that has a single dimension 510 that encompasses thedata from referencing “Customer” table 300 of FIG. 3 and from referenced“Location” table 400 of FIG. 4, data cube 700 has the aforementionedreferencing dimension 710 and referenced dimension 712. Accordingly,data from the referenced “Location” table 400 is not replicated multipletimes in the referencing “Customer” dimension 710. Such elimination ofrepetition results in a reduction of data storage size and a reductionof the time required for modeling the cube. These advantages areincreased when the referenced dimension 712 is referenced by more thanone referencing dimension.

[0039] Referencing “Customer” dimension 710 has a dimension hierarchyincluding each attribute 310, 312 from referencing “Customer” table 300of FIG. 3. The dimension hierarchy of referencing “Customer” dimension710 is represented by data tree 800 as shown in FIG. 8. As shown, nodes810-817 in the top row are “Location ID” nodes corresponding to“Location ID” attribute 312 in FIG. 3. Nodes 820-829 in the second roware “Customer ID” nodes corresponding to “Customer ID” attribute 310 inFIG. 3.

[0040] Referenced “Location” dimension 712 has a dimension hierarchyincluding each attribute 410-414 from referenced “Location” table 400 ofFIG. 4. The dimension hierarchy of referenced “Location” dimension 712is represented by data tree 900 as shown in FIG. 9. Nodes 910-912 in thefirst row are “State” nodes corresponding to “State” attribute 414 inFIG. 4. Nodes 920-924 in the second row are “City” nodes correspondingto “City” attribute 412 in FIG. 4. Nodes 930-937 in the third row are“Location ID” nodes corresponding to “Location ID” attribute 410 in FIG.4. As should be appreciated, cube 700 may be used to analyze queriesaggregated by any row of nodes in “referencing” data tree 800 or“referenced” data tree 900. For example, total purchase quantity bystate or city may be analyzed, where the relationship between purchasequantity and state or city is through customer.

[0041] Thus, a system and method for analytically modeling data with areferenced attribute is disclosed. The system and method of the presentinvention eliminate the repetition associated with existing methods inwhich referenced attributes are included in the dimension hierarchy ofeach dimension of the cube. Such existing methods are repetitive becausemultiple data entries corresponding to a referenced attribute may haveidentical values. For example, with regard to the “Customer” and“Location” tables, multiple customers may reside in the same stateand/or city. This repetition is exacerbated when multiple referencingtables refer to a single referenced table. The system and method of thepresent invention eliminate the repetition by employing a referencingdimension that references a referenced dimenison.

[0042] The programming necessary to effectuate the processes performedin connection with the present invention is relatively straight-forwardand should be apparent to the relevant programming public. Accordingly,such programming is not attached hereto. Any particular programming,then, may be employed to effectuate the present invention withoutdeparting from the spirit and scope thereof.

[0043] While the invention has been described and illustrated withreference to specific embodiments, those skilled in the art willrecognize that modifications and variations may be made withoutdeparting from the principles of the invention as described above andset forth in the following claims. For example, while the invention hasbeen described with reference to a “Purchase”, “Customer”, and“Location” tables, the invention may be used in conjunction with anytable from a relational database. Furthermore, the analytical datamodels of the present invention may comprise any number of dimensionscorresponding to any number of data attributes. Accordingly, referenceshould be made to the appended claims as indicating the scope of theinvention.

We claim:
 1. A method in combination with first, second, and thirdtables of data, the first table organizing a first type according to afirst attribute, the second table organizing a second type according tothe first attribute and a second attribute, the third table organizing athird type according to a third attribute, the second attribute of thesecond table being related to the third attribute of the third tablesuch that the second table may be cross-referenced to the third tablethereby, the method comprising: modeling a measure according to thefirst type of the first table; modeling a first dimension according tothe second type of the second table; modeling a second dimensionaccording to the third type of the third table; tying the measure to thefirst dimension according to the first attribute of the first and secondtables to allow the measure to be analyzed by the first dimensionaccording to the first attribute; and tying the first dimension to thesecond dimension according to the second attribute of the second tableand the third attribute of the third table to allow the measure to beanalyzed by the second dimension according to the second attribute ofthe second table and the third attribute of the third table, whereby thefirst dimension references the second dimension.
 2. The method of claim1, comprising modeling a measure according to the first type of thefirst table, the first table comprising data stored in a relationaldatabase.
 3. The method of claim 1, comprising modeling a firstdimension according to the second type of the second table, the secondtable comprising data stored in a relational database.
 4. The method ofclaim 1, comprising modeling a second dimension according to the thirdtype of the third table, the third table comprising data stored in arelational database.
 5. The method of claim 1, comprising modeling afirst dimension according to the second type of the second table, thesecond table having data organized hierarchically therein.
 6. The methodof claim 5, further comprising aggregating the data of the firstdimension according to the hierarchical organization of the secondtable.
 7. The method of claim 1, comprising modeling a second dimensionaccording to the third type of the third table, the third table havingdata organized hierarchically therein.
 8. The method of claim 5, furthercomprising aggregating the data of the second dimension according to thehierarchical organization of the third table.
 9. The method of claim 1,comprising modeling a measure of a data cube according to the first typeof the first table.
 10. The method of claim 9, comprising modeling ameasure of a data cube formatted for online analytical processingaccording to the first type of the first table.
 11. The method of claim1, comprising modeling a first dimension of a data cube according to thesecond type of the second table.
 12. The method of claim 11, comprisingmodeling a first dimension of a data cube formatted for onlineanalytical processing according to the second type of the second table.13. The method of claim 1, comprising modeling a second dimension of adata cube according to the third type of the third table.
 14. The methodof claim 13, comprising modeling a second dimension of a data cubeformatted for online analytical processing according to the third typeof the third table.
 15. A computer readable medium having stored thereoncomputer readable instructions in combination with first and secondtables of data, the first table organizing a first type according to afirst attribute, and a second attribute and the second table organizinga second type according to a third attribute, the first attribute of thefirst table being related to the third attribute of the second tablesuch that the first table may be cross-referenced to the second tablethereby, the second attribute of the first table being related to thethird attribute of the second table such that the first table may becross-referenced to the second table thereby, the computer readableinstructions for performing the following steps: modeling a measureaccording to the first type of the first table; modeling a dimensionaccording to the second type of the second table; tying the measure tothe dimension according to the first attribute of the first table andthe third attribute of the second table to allow the measure to beanalyzed by the dimension according to the first attribute; and tyingthe first dimension to the second dimension according to the secondattribute of the first table and the third attribute of the second tableto allow the measure to be analyzed by the second dimension according tothe second attribute of the second table, whereby the first dimensionreferences the second dimension.
 16. The computer readable medium ofclaim 15, comprising instructions for performing the step of modeling ameasure according to the first type of the first table, the first tablecomprising data stored in a relational database.
 17. The computerreadable medium of claim 15, comprising instructions for performing thestep of modeling a first dimension according to the second type of thesecond table, the second table comprising data stored in a relationaldatabase.
 18. The computer readable medium of claim 15, comprisinginstructions for performing the step of modeling a second dimensionaccording to the third type of the third table, the third tablecomprising data stored in a relational database.
 19. The computerreadable medium of claim 15, comprising instructions for performing thestep of modeling a first dimension according to the second type of thesecond table, the second table having data organized hierarchicallytherein.
 20. The computer readable medium of claim 19, furthercomprising instructions for performing the step of aggregating the dataof the first dimension according to the hierarchical organization of thesecond table.
 21. The computer readable medium of claim 15, comprisinginstructions for performing the step of modeling a second dimensionaccording to the third type of the third table, the third table havingdata organized hierarchically therein.
 22. The computer readable mediumof claim 21, further comprising instructions for performing the step ofaggregating the data of the second dimension according to thehierarchical organization of the second table.
 23. The computer readablemedium of claim 15, comprising instructions for performing the step ofmodeling a measure of a data cube according to the first type of thefirst table.
 24. The computer readable medium of claim 23, comprisinginstructions for performing the step of modeling a measure of a datacube formatted for online analytical processing according to the firsttype of the first table.
 25. The computer readable medium of claim 15,comprising instructions for performing the step of modeling a firstdimension of a data cube according to the second type of the secondtable.
 26. The computer readable medium of claim 25, comprisinginstructions for performing the step of modeling a first dimension of adata cube formatted for online analytical processing according to thesecond type of the second table.
 27. The computer readable medium ofclaim 15, comprising instructions for performing the step of modeling asecond dimension of a data cube according to the third type of the thirdtable.
 28. The computer readable medium of claim 27, comprisinginstructions for performing the step of modeling a second dimension of adata cube formatted for online analytical processing according to thethird type of the third table.
 29. A system in combination with firstand second tables of data, the first table organizing a first typeaccording to a first attribute, and a second attribute and the secondtable organizing a second type according to a third attribute, the firstattribute of the first table being related to the third attribute of thesecond table such that the first table may be cross-referenced to thesecond table thereby, the second attribute of the first table beingrelated to the third attribute of the second table such that the firsttable may be cross-referenced to the second table thereby, the systemcomprising: a processor operative to execute computer executableinstructions; and memory having stored therein computer executableinstructions for performing the following steps: modeling a measureaccording to the first type of the first table; modeling a dimensionaccording to the second type of the second table; tying the measure tothe dimension according to the first attribute of the first table andthe third attribute of the second table to allow the measure to beanalyzed by the dimension according to the first attribute; and tyingthe first dimension to the second dimension according to the secondattribute of the first table and the third attribute of the second tableto allow the measure to be analyzed by the second dimension according tothe second attribute of the second table, whereby the first dimensionreferences the second dimension.
 30. The system of claim 29, comprisingcomputer executable instructions for performing the step of modeling ameasure according to the first type of the first table, the first tablecomprising data stored in a relational database.
 31. The system of claim29, comprising computer executable instructions for performing the stepof modeling a first dimension according to the second type of the secondtable, the second table comprising data stored in a relational database.32. The system of claim 29, comprising computer executable instructionsfor performing the step of modeling a second dimension according to thethird type of the third table, the third table comprising data stored ina relational database.
 33. The system of claim 29, comprising computerexecutable instructions for performing the step of modeling a firstdimension according to the second type of the second table, the secondtable having data organized hierarchically therein.
 34. The system ofclaim 33, further comprising computer executable instructions forperforming the step of aggregating the data of the first dimensionaccording to the hierarchical organization of the second table.
 35. Thesystem of claim 29, comprising computer executable instructions forperforming the step of modeling a second dimension according to thethird type of the third table, the third table having data organizedhierarchically therein.
 36. The system of claim 35, further comprisingcomputer executable instructions for performing the step of aggregatingthe data of the second dimension according to the hierarchicalorganization of the second table.
 37. The system of claim 29, comprisingcomputer executable instructions for performing the step of modeling ameasure of a data cube according to the first type of the first table.38. The system of claim 37, comprising computer executable instructionsfor performing the step of modeling a measure of a data cube formattedfor online analytical processing according to the first type of thefirst table.
 39. The system of claim 29, comprising computer executableinstructions for performing the step of modeling a first dimension of adata cube according to the second type of the second table.
 40. Thesystem of claim 39, comprising computer executable instructions forperforming the step of modeling a first dimension of a data cubeformatted for online analytical processing according to the second typeof the second table.
 41. The system of claim 29, comprising computerexecutable instructions for performing the step of modeling a seconddimension of a data cube according to the third type of the third table.42. The system of claim 41, comprising computer executable instructionsfor performing the step of modeling a second dimension of a data cubeformatted for online analytical processing according to the third typeof the third table.